Seamless indoor navigation is an important user care-about and an industry technological goal. Seamless indoor navigation is especially important when availability of navigation satellites is absent or becomes absent in a large building and/or an urban canyon. Sensor-aided pedestrian navigation is a key enabler for a seamless indoor navigation solution. GPS satellites and other types of positioning satellites are not visible indoors, and GPS can often misguide in an urban canyon. Low-cost MEMS sensors (accelerometers, gyroscopes, E-compass) are making inroads into and being included in mobile phones and other devices. (MEMS refers to micro-electromechanical system technology.)
However, classical inertial navigation system (INS) based solutions are often not suitable, due to error buildup due to poor performance of inexpensive MEMS sensors. Conventionally, a classical INS solution uses a high precision accelerometer, E-compass, and gyroscope. Distance s can be estimated by double integration of accelerometer measurements a over time according to Equation (1):s=s0+u0Δt+0.5aΔt2  (1)
Accurate position estimation using this double integration approach depends on the availability of data specifying a known initial position s0 and known initial velocity vector u0, and for practical purposes would likely involve starting from rest so that the initial velocity vector is zero (0, 0, 0). Also, this double integration approach likely would need expensive high precision accelerometers and would suffer from large error growth or accumulation (quadratic over time) due to high bias variations and noise impairments in low-cost MEMS sensors.
Alternatively, suppose pedestrian navigation were attempted by estimating the distance traveled and user direction, where distance traveled equals number of steps detected times step length. Slow-walk and unstrapped pedestrian navigation using low cost sensors for mobile applications are believed to be unsolved problems in or among several problems that have been less-than-successfully struggled with, both in the areas of step detection and walking direction estimation. Robust step detection that is accurate in various scenarios is important, as it directly impacts the accuracy of estimated distance. Pedestrian navigation use cases involve slow walk and normal walk scenarios among others. If accelerometer measurements are used to try to detect the number of steps, then slow walk scenarios especially for a handheld or body-worn pedometer or pedestrian navigation device continue to be a challenging problem for step detection—both because of the low accelerations involved and because of the more complex acceleration waveforms encountered in slow walking. Also, some pedometers focus on fitness applications (running, jogging, etc).
If step detection were to use threshold detection of acceleration excursions, it would be prone to false or erroneous step detection, as any unintended motion of the mobile device unrelated to steps by the user would trigger such threshold detection. If relatively large thresholds for step detection were used, the step detector would quite likely underestimate the number of steps during slow walks. Step detection would likely also be triggered under vehicular scenarios when the user is sitting in a vehicle and is jostled, so this approach is not robust for navigation applications. Thus, jerks or vibrations also satisfying the threshold crossing detection condition of such a sensor would also register erroneous step detections. Counting errors would also be introduced by waiting for a certain number of steps or a certain period of time to avoid detecting short jerks before triggering a detection by starting or resuming counting thereafter.
Moreover, satisfactorily estimating the user walking direction is believed to have baffled attempts hitherto because a device might be strapped on the person (belt, shirt, trousers), or be quasi-unstrapped like a handheld, or even fully unstrapped (swinging hands). Compounding the challenge in pedestrian navigation is that the user is moving geographically in a manner generally uncoupled with the attitude or orientation of the device with respect to the user, as well as that the device is generally uncoupled in its orientation or heading with respect to the geography.
Accurate pedestrian navigation using low cost sensors for mobile applications requires accurate step detection and accurate walking direction sensing in numerous pedestrian use cases. Classical INS (inertial navigation) is problematic as already noted because the biases vary a lot and rapidly introduce large errors into estimation of the actual displacement. Accurately detecting walking steps and their repetition period is absent in or problematic for conventional low-cost pedometer units. In addition, the orientation of an accelerometer, if an accelerometer is used, would be unknown—making it difficult to resolve forward, vertical and lateral human body accelerations, due to high bias and gain variations seen in the low cost MEMS sensors. Further complicating the subject, a user can hold the device in any position, like in shirt or pant pocket, or strapped to belt, or held in a hand. Moreover, the user could be walking slowly, fast or jogging or running.
Accordingly, significant technological departures to somehow address and solve these and other problems are needed and would be most desirable.